Energy sample forecasting is an important function for most facilities. Whether large or small, most facilities include devices that utilize energy, such as heating, venting and air conditioning-refrigeration (HVAC-R) systems and the like. The amount of energy utilized by such systems may vary depending on external factors, such as the severity and degree of outside air temperature (hot or cold), a type of weather pattern being experienced, internal load, need for running multiple systems in the facility, and so on.
Since energy usage can vary significantly depending on such internal and external factors, it is beneficial for the facilities to be able to anticipate future energy usage so that the energy usage can be managed. In conventional methods to model energy consumption, dynamics of control system(s) of a HVAC-R system is captured, evolution of control system states are tracked, and then the system states are mapped to the energy consumption. However, control choices in the control systems are typically proprietary and tuned locally to a facility by an installation vendor. Even if the system states can be tracked in terms of the controlled variables, it may be difficult to calibrate such a control system model (which runs every few seconds) from sensor information that is typically logged every few minutes. In some cases, sensors for detecting the information may not be available in the facilities.